Project Summary A wealth of data over the past few years, from our lab and others, has demonstrated that the mitochondrial morphology of tumor cells is distinct from normal cells. Furthermore, a number of studies have shown that genetic or pharmacological manipulation of the machinery that regulates mitochondrial morphology can impact a variety of tumorigenic processes. Indeed, in work performed for the parent grant of this proposal, we have shown that genetic inhibition of the mitochondrial fission GTPase Drp1 can block pancreatic tumor growth and increase survival in a genetically engineered mouse model. This work has led to the deeper question of how changes in mitochondrial shape, which ultimately result from a combination of both genetic and environmental influences, contribute to the physiological processes that drive tumor growth. This question has been difficult to ask using traditional genetic and pharmacological approaches due to the complexity of the signaling pathways that converge on the mitochondria and the inherent heterogeneity present within the tumor and its surrounding stroma. To that end, we have developed a new software package designed to catalogue the morphological features of mitochondria within cells in culture or in fixed tissue. By using this software to analyze the tumor cells developed in our mouse models of pancreatic cancer, we propose to determine the relationship between specific mitochondrial features and key physiological attributes of these tumors. To do this, we have developed a machine learning technique, validated against a panel of tumor derived pancreatic cell lines with genetically-induced mitochondrial heterogeneity, capable of identifying relationships between mitochondrial features, or combinations of mitochondrial features, and other attributes of those cells. This approach will allow us to leverage the wealth of phenotypic data we have collected from our tumors with the data we are now able to collect on the mitochondrial heterogeneity, either between or within those tumors, in order to identify the role that mitochondrial heterogeneity plays in tumor growth, regardless of whether that heterogeneity arises from manipulation of the mitochondrial dynamics machinery or whether it arises from the myriad influences within the tumor environment. Successful completion of these aims will provide critical insights into the role that mitochondrial heterogeneity plays in pancreatic tumor growth and also pave the way for future analysis of mitochondrial heterogeneity in other tumor types.